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| Section | Weight | Objectives |
|---|---|---|
| AI Life Cycle Risk Management | 21% | - AI Data and Asset Management - AI Design, Development/Procurement, and Documentation - AI Implementation, Maintenance, and Decommissioning - AI Model Training, Testing, and Validation |
| AI Risk Program Management | 42% | - AI Risk Response and Mitigation - AI Risk Identification and Assessment - AI Risk Monitoring and Reporting - AI Risk Assurance and Continuous Improvement |
| AI Risk Governance and Framework Integration | 37% | - AI Trustworthiness, Ethical and Societal Implications - AI Organizational Processes and Alignment - AI Models, Frameworks, Strategies, and Use Cases - AI Regulatory Compliance and Legal Considerations - AI Ownership, Oversight, and Accountability - AI Policies, Procedures, and Organizational Training |
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NEW QUESTION # 177
Which of the following is the BEST way to integrate AI risk management into operational procedures?
Answer: C
Explanation:
Embedding AI risk management into operations requires that risk assessment activities be integrated throughout the AI development and deployment life cycle, not applied only at discrete checkpoints. This life cycle integration ensures risks are identified and addressed at the stages where they can be most effectively mitigated.
Why C is Correct: The ISACA AAIR curriculum identifies life cycle-integrated risk assessment as the most effective operational integration approach. By introducing risk assessment stages throughout development and deployment-at design, data collection, model training, testing, and deployment-organizations catch risks before they are built into the system. This proactive approach is far more effective than retrospective assessment.
Why A is Wrong: Organization-wide training increases risk awareness but represents an enabler rather than an operational integration mechanism. Training alone does not embed risk practices into workflows.
Why B is Wrong: Third-party audits provide periodic independent assurance but occur infrequently and reactively. They cannot substitute for continuous, integrated risk assessment throughout operations.
Why D is Wrong: Requiring risk committee approval for automation changes creates a governance checkpoint at one decision point. This is narrower than integrating risk assessment across all development and deployment stages and may create bottlenecks without proportionate risk management benefit.
NEW QUESTION # 178
An organization is launching multiple generative AI use cases. The risk practitioner needs to ensure that AI risk decisions align with business objectives and approved risk boundaries of the enterprise.
What is the best action to take?
Answer: A
Explanation:
Within the ISACA Advanced in AI Risk framework, governance decisions should align AI use with policy, accountability, stakeholder expectations, risk appetite, and applicable legal or ethical obligations. AI risk should be explicitly included in enterprise risk appetite and tolerance so use cases are judged against approved business boundaries. A risk register and data controls are useful, but appetite and tolerance connect treatment decisions directly to business objectives. This makes option D, Include AI in risk appetite and tolerance, the strongest answer. The other choices describe narrower technical, operational, performance, or administrative considerations and do not address the primary risk-management objective in the scenario as directly. A risk practitioner should select the response that most effectively reduces the stated exposure while preserving appropriate oversight, traceability, and alignment with organizational risk tolerance and business requirements.
NEW QUESTION # 179
Which of the following is the MOST critical component of a disaster recovery plan (DRP) for data, models, and AI infrastructure?
Answer: A
Explanation:
Within the ISACA Advanced in AI Risk framework, program management connects risk identification, control selection, treatment, monitoring, resilience, third-party oversight, and reporting to enterprise risk objectives. Disaster recovery for AI requires tested restoration processes for production-ready models and supporting artifacts, with version control and remote or segregated storage. Policies and insurance do not restore systems, while broader supply-chain issues belong to continuity planning. This makes option C, Restoration processes for production-ready AI models with version control and remote storage, the strongest answer. The other choices describe narrower technical, operational, performance, or administrative considerations and do not address the primary risk-management objective in the scenario as directly. A risk practitioner should select the response that most effectively reduces the stated exposure while preserving appropriate oversight, traceability, and alignment with organizational risk tolerance and business requirements.
NEW QUESTION # 180
Which of the following is the BEST justification for selecting a risk avoidance strategy when considering whether to deploy a high-impact AI system?
Answer: D
Explanation:
Risk avoidance is the risk treatment strategy of not engaging in an activity because the risks it presents cannot be adequately mitigated to within acceptable tolerance. For high-impact AI systems, the justification for avoidance must be proportionate to the gravity of the decision to forgo deployment entirely.
Why A is Correct: The ISACA AAIR risk treatment framework identifies potential harm to stakeholders as the most compelling justification for risk avoidance in AI deployment decisions. When a high-impact AI system poses risks of significant harm to individuals, communities, or society that cannot be adequately controlled, avoiding deployment is the ethically and legally appropriate choice. Stakeholder harm-especially irreversible or widespread harm-represents the highest severity risk outcome and justifies the most conservative risk treatment.
Why B is Wrong: Cost reduction objectives are business case considerations, not risk management justifications. Avoiding deployment to reduce costs is a financial decision, not a risk avoidance strategy. Risk avoidance decisions are driven by harm potential, not cost efficiency.
Why C is Wrong: Staff expertise shortages represent an organizational capability constraint that can be addressed through hiring, training, or managed services. A capability gap is a surmountable operational challenge, not a justification for permanently avoiding a valuable deployment.
Why D is Wrong: Data poisoning attack likelihood is a security risk that can be mitigated through appropriate controls-data integrity verification, provenance tracking, anomaly detection. A manageable risk with available mitigations does not justify full risk avoidance when stakeholder harm is not at stake.
NEW QUESTION # 181
Which of the following is the MOST significant risk when an organization depends on a single AI vendor ' s proprietary environment?
Answer: D
Explanation:
Within the ISACA Advanced in AI Risk framework, program management connects risk identification, control selection, treatment, monitoring, resilience, third-party oversight, and reporting to enterprise risk objectives. Dependence on a single proprietary AI environment can create vendor lock-in, making migration technically difficult, operationally disruptive, and expensive. Transparency and scalability are relevant concerns, but prohibitive switching costs are the central dependency risk. This makes option B, Prohibitive migration costs, the strongest answer. The other choices describe narrower technical, operational, performance, or administrative considerations and do not address the primary risk-management objective in the scenario as directly. A risk practitioner should select the response that most effectively reduces the stated exposure while preserving appropriate oversight, traceability, and alignment with organizational risk tolerance and business requirements.
NEW QUESTION # 182
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